Automated Seizure Monitoring
Neurons are autorhythmic and may fire intrinsically hundreds of times a second
(Hopfield, 1999; Goldensohn & Purpura, 1963). In order to process information inhibitory
networks suppress neural autorhythmicity and synchronize functional units of cortex (Steriade &
Llinas, 1988), which are considered to be sub-millimeter-diameter minicolumns (Mountcastle,
1957; 1978; Casanova & Tillquist, 2008). These modules may themselves become synchronized
with other minicolumns until sizable regions of the brain are recruited (Calvin, 1995). Such
intermodular synchronization reflects membership within an even larger functional unit
(Mountcastle, 1997; Gerloff et al., 2006; Tiesinga & Sejnowski, 2004). Intermodular
synchronization may be detected at the scalp when as little as 6 cm2 of cortical tissue is involved
(Cooper et al., 1965). Synchrony between cortical areas, however, must remain modest for
information processing. Hypersynchrony between brain areas produces nonresponsiveness, states
such as sleep and seizure (Timofeev & Steriade, 2002), see Figure 1. Seizure occurs when
healthy network inhibitory processes are disrupted by acquired (e.g., traumatic injury, cortical
dysplasia) or idiopathic and genetic factors, and the excitatory/inhibitory homeostasis is replaced
by hyperpolarizing (EEG wave) and depolarizing (EEG spike) events. As neurons become
synchronized into a hyperexcitable firing pattern, waves and spikes of excitation spread intraand interhemispherically and we observe a generalized seizure.
Cortical potentials reflect proportion of neurons inhibited compared to those that remain
autonomous at a given frequency (Nunez et al., 2001; Silberstein, 2004). Spectral magnitude is a
summary of this inhibition across time and spectral power an estimate of its variance (Tenke &
Wavestate, Inc. – Automated Seizure Monitoring – page 1 Kayser, 2005). Whereas magnitude and power reflect local de/activation, coherence and
comodulation are models of magnitude and phase synchrony between electrode sites,
respectively. Phase synchrony may be estimated by convolution with a complex wavelet
(Goodman, 1957) or with a Hilbert transform which ignores signal strength differences (e.g.,
Boeijinga & Lopes da Silva., 1989), or through techniques such as mutual information (e.g.,
Lopes da Silva et al., 1989), cross-correlation (Foster & Guinzey, 1967; Brazier & Casby, 1951)
or general synchronization algorithms (Arnhold et al., 1999; Stam & van Dijk, 2002; David et
al., 2004). Magnitude synchrony, which is known as comodulation in the single frequency
spectrum and bimodulation in the bifrequency spectrum, is largely estimated with a mean crossproduct of normalized magnitude over time, i.e., a Pearson product moment correlation (1896) of
spectral magnitude (Sterman & Kaiser, 2001). Magnitude and phase synchrony can be
independent (orthogonal) properties, although in the brain this is rarely the case, see Figure 2.
Figure 1. Hypersynchrony between sites is evident in coherence trends (computed using a short history of 10 s).
Wavestate, Inc. – Automated Seizure Monitoring – page 2 Figure 2. Phase and magnitude synchrony dissociated in two pairs of signals.
EEG spectral magnitude or its square (power) are commonly analyzed, see Figure 3 for
example; but we can also just as readily characterize brain activity with trend coefficients,
variance coefficients, trend variance coefficients (residual variance), or any linear or nonlinear
tendency including median voltage, modal frequency, or spectral edge (Drake et al., 1996), as
long as this parameter is empirically associated with un/consciousness or seizure
phenomenology. With any formulation we may also compress or amplify one side of our mindbrain correspondence by applying a logarithm, exponent, or other function to any measurement
in order to improve correspondence or statistical strength, see Figure 4. Nunez et al. (1997)
concludes that "studies of coherence and brain state should include several different kinds of
estimates to take full advantage of information in recorded signals." Toward this end a
periodicity table was proposed by Kaiser (2007; in press), a general framework that organizes
spectral parameters by number of signals, frequencies, and other features. Table 1 presents
measures of activity and connectivity familiar to clinical EEG as well as other synchrony indices
appropriated or refined from other fields such as electroacoustics, seismography, and information
sciences.
Wavestate, Inc. – Automated Seizure Monitoring – page 3 Figure 3. Spectral trend often reveals a seizure but unreliably so.
Figure 4. Comodulation and coherence distributions are normalized by Fisher-z transformation in this example
(alpha band of all site pairings in 13 subjects). A Fisher-z transform adjusts probabilities as perfect coupling is
approached, incorporating how more order is required to move towards perfect correlation than disorder is needed to
move the same distance away.
Spectral magnitudes of human EEG were of interest to Berger (1929) and were quantified
early (Dietsch, 1932; Rohracher, 1937). Correlation of spectral magnitude was investigated early
Wavestate, Inc. – Automated Seizure Monitoring – page 4 on (Larsen, 1969; Barcaro et al., 1986), but it was largely overshadowed in favor of coherence
and other forms of linear dependence (Shaw, 1981). Most EEG research on correlated signals did
not focus on spectral activity (voltage periodicities) but voltages themselves (time domain
analysis) (Brazier & Casby, 1952; Shaw, 1974; Evans, 1977; Guevara & Corsi-Cabrera, 1996)
and even textbooks conflated coherence with spectral correlation (Priestley, 1981). Cortical
neurons undergo changes in temporal relations between groups as cells progressively
synchronized (Steriade & Amzica, 1994). Our goal is to detect changes in phase and magnitude
synchrony prior to or in lieu of generalized and bilaterally synchronous activity given the
compromised EEG activity of neurotrauma patients.
Seizures are common in neurotrauma patients and often non-convulsive. An ICU patient
can be unconscious with no overt signs of seizing. Untreated seizures are known to cause brain
injury. Seizures are routinely reviewed by epileptologists. Their judgment is based on
morphological (waveform), evolutionary (time-variation), and hypersynchronous (unusual
synchrony) aspects of the EEG. Clinical assessment is entirely based on the characteristics of the
waveform pattern observed. Commercial seizure detection programs employ either waveform
dependent or waveform independent algorithms. Waveform algorithms require tuning of the
algorithm by the staff; an example seizure from an individual patient is provided and the
algorithm searches for similar occurrences. Waveform independent algorithms do not require
such tailoring to an individual but rely on general parameters to detect seizure. Only the latter
technique can be used in preventative EEG monitoring, in which a patient has not had a seizure
but is likely to do so. Waveform dependent techniques are useful in certain clinical situations, i.e.
long term monitoring of epilepsy patients where the necessary time to identify an individual's
EEG profiles is available before a seizure, but this technique is impractical for the ICU.
Wavestate, Inc. – Automated Seizure Monitoring – page 5 Automatic detection schemes are highly desirable and people have worked on detection
schemes for more than 30 years. Commercial systems are limited compared to those attempted
and validated in the scientific literature. Seizure detection algorithms falls into two general
approaches, patient specific (tailored) and patient-independent techniques, often called
quantitative methods. The former requires scoring of each patient’s record before detection can
begin and the latter are more general detection schemes.
Seizure detection techniques
Patient-independent
1. Power spectrum coefficients
2. Spectral variance analysis
3. Time frequency analysis
4. Synchronization
5. Entropy
6. Lyapunov exponents
7. Wavelet transform
Patient-dependent
8. Waveform templating
1. Power spectrum information is typically summarized by total power, median frequency or
spectral edge. Peak fluctuations in specific frequency are used to identify epileptic seizures in
some cases) and Wavestate employs a systematic process for evaluating peak fluctuations
which includes not only periodicity of spectral magnitude (FFT of spectral trend) but of
spectral variability, autoherence, and automodulation. All four indices identify epileptic
activity in status epilepticus patients.
2. Variance of EEG activity is usually calculated in consecutive nonoverlapping windows
which fails to account for half the signal due to data functions necessary for spectral analysis
(e.g., cosine tapering windows). Wavestate evaluates variance with periodicity analysis
(further fourier analysis of spectral trends) and uses overlapped windows to ensure a timefrequency unity function.
Wavestate, Inc. – Automated Seizure Monitoring – page 6 3. Time frequency analysis evaluates how spectral parameters evolve over time, which may
be characterize with continuous functions such as autocorrelation (automodulation,
autoherence).
4. Synchronization algorithms detect phase synchrony between signals. This approach may
statistically delineate epileptiform activity from normal EEG but with an unacceptable false
detection rate, making it impractical for a clinical device.
5. Entropy of a discrete probability distribution is an estimate of its complexity in terms of
magnitude or phase.
Figure 5. Use of entropy to identify seizure. Entropy of 14-16 Hz increases during seizure and sharply decreases in
2-4 Hz.
6. Lyapunov exponents quantify phase stability of low-dimensional dynamical systems.
7. Wavelet transform can be performed along different scales or resolutions. Any time series
function can be used as a kernel of investigation including functions that approximate
prototypical spike-wave activity. Fourier analysis is a special case of wavelet transform that
relies on sine and cosine (trigonometric) functions.
8. Waveform prototype matching requires a training phase in which an artificial neural
network is provided a seizure example from an individual patient (or as much as a day’s
record with seizure scored by an expert) in order to detect future seizures he or she may have.
This approach cannot detect the first seizure, making it impractical for anticipatory seizure
monitoring.
Wavestate, Inc. – Automated Seizure Monitoring – page 7 Many researchers employed artificial neural networks regardless of the parameter being used
in detection, although training a network requires signal-rich data and even with extensive
training, false detection rates may remain high (e.g., 1.5 per hour). Wavelet decomposition of
patient data exploits consistency of an individual patient's seizure and non-seizure EEG but such
a technique is useful for surgeries where the necessary time to identify an individual's EEG
profiles are available before a seizure, but do not suit ICU practices.
Table 1. Techniques used in commercial seizure detection
Description of selected spectral parameters on the periodicity table.
Local activity (column 1)
Autophase refers to “phase slippage,” mean phase difference of a given frequency from one moment (epoch) to the
next.
Biamplitude is spectral activity of a given frequency compared to another, such as a theta power-to-beta power
ratio.
Spectral entropy is the (logarithmically-compressed) number of possible arrangements inherent in a signal. As
entropy decreases, so does the level of consciousness.
Phase entropy is phase (combinatorial) probability.
Stability of activity (column 2).
Coefficient of variation is standard deviation of spectral activity divided by the average over time. Other measures
of variability include standard deviation by itself and trend consistency.
Wavestate, Inc. – Automated Seizure Monitoring – page 8 Automodulation is magnitude consistency of a given frequency across time.
Autoherence is phase consistency of a given frequency across time. Bimodulation is magnitude consistency between frequencies. Certain bimodulation coefficients may reflect changes
in blood flow or glucose utilization .
Figure 6. Moderate bimodulation of theta and alpha is observed in this short record. Theta magnitudes correlate
well with alpha magnitude but neither correlate well with gamma magnitude across time. If spectral trends were
generated by different sites, this would be cross-bimodulation or cross–trimodulation.
Trimodulation is magnitude consistency among frequencies, i.e., mean normalized round-robin cross-product.
Triherence is phase consistency among frequencies.
Network state or connectivity (column 3)
Cross-biamplitude compares spectral activity at a given frequency to activity in another frequency at another site at
the same time.
Network stability (column 4)
Reversal variability is a non-spectral measure of variability (standard deviation) of amplitude reversal in adjacent
bipolar channels.
Cross-bimodulation is magnitude consistency of a given frequency at one site compared to another frequency at
another site.
Cross-bicoherence is phase consistency of a given frequency at one site compared to another frequency at another
site.
Joint entropy modulation is a measure of mutual information stability of two signals in terms of spectral
magnitude.
Joint phase entropy modulation is a measure of mutual phase stability between signals.
System state (column 5)
Wavestate, Inc. – Automated Seizure Monitoring – page 9 Rogue site analysis (RSA) is magnitude individuation across time. Magnitude is auto-normalized and compared to
all sites at each moment of time. A site which is least like all others is termed rogue and percent of time spent rogue
is tallied.
Rogue site phase analysis (RSPA) refers to phase individuation across time Each site is compared to all others on
autophase.
Site biamplitude is mean ratio of spectral activity at a given frequency compared to mean spectral activity for the
entire head at another frequency.
Rogue frequency analysis (RFA) is a means to estimate independence across topography and spectrum, the
maximum percent time spent rogue for auto-normalized magnitudes at any frequency.
System stability (column 6)
Peak autocorrelation is a non-spectral technique to identify which site possesses the most amplitude (voltage)
periodicity.
Site bimodulation is spectral activity at a given frequency correlated with mean spectral activity for all other sites at
another frequency.
Seizure is detected within a single EEG channel by creating a weighted score computed
every 250 ms based on increased low frequency magnitude, increased low frequency autophase,
increased low frequency entropy and decreased moderate and high frequency entropy, increased
coefficient of variation (normalized variability), increased low frequency autoherence, along
with specific frequency-pair bimodulation increasing at the end of a seizure. Hypersynchrony
between EEG signals is a computed using comodulation and coherence, joint entropy
modulation, and rogue site analysis among other factors.
METHOD
Participants: EEG was acquired from 13 seizure patients in the UCLA Medical Center
Neurointensive Care facility (mean age 41.8 y, 8 women, 5 men) between 2001 and 2006, a
representative sample of seizure patients seen in this unit.
Wavestate, Inc. – Automated Seizure Monitoring – page 10 Figure 7. ICU patients at UCLA Medical Center (7W) from 2003 to 2006.
Materials: EEG was recorded with Voyageur Nicolet acquisition unit (Viasys Healthcare Inc.,
Conshohocken, PA) with a 16-bit A/D converter. Signals were digitized at 256 samples per s or
higher and down-sampled and displayed 128 times per s. High and low pass filters were set at
0.1 Hz and 60 Hz. Common mode rejection ratio was 90 dB at 60 Hz. Twelve channel
topographic EEG was acquired with needle electrodes and referenced to a single electrode
located near C3 in most cases. Electrode positions followed the International 10-20 electrode
placement system (Jasper, 1958).
Procedure: EEG was acquired at UCLA Medical Center Neurointensive Care facility. Electrode
impedances were generally kept below 5 K Ohms.
Data Analysis: EEG records were inspected for artifacts and contaminated segments were
eliminated prior to analysis. If artifact was present in any channel, data from all recording
channels were ignored for its duration. As little as 10 ms could be eliminated.
Results
Seizure was detected in EEG by means of comodulation and coherence indices along with phase
Wavestate, Inc. – Automated Seizure Monitoring – page 11 lag, joint entropy, autocorrelation of joint entropy, and rogue site analysis.
Figure 1. Automodulation is significantly higher in low frequencies during seizure.
Figure 9. Spectral entropy means of 6 seizures and 12 inter-ictal episodes of a status patient.
Wavestate, Inc. – Automated Seizure Monitoring – page 12 Figure 10. Bimodulation 3 hz and 10 Hz magnitude detects end of seizures.
Wavestate, Inc. – Automated Seizure Monitoring – page 13 Figure 11. Entropy ratio of 9 and 10 Hz compared to 2 and 3 Hz is high during ictal events.
Discussion
EEG is highly sensitive indicator of CNS ischemia or hypoxia (Sundt et al., 1981) and it
provides quantifiable, accurate, and constant monitoring of a patient’s electrophysiology as well
as depth of consciousness (Sarkela et al, 2002); however, the amount of information provided by
this technology makes it impractical for human review. Automated computerized analysis of
EEG is not only a dream of physicians for decades (Bodenstein & Praetorius, 1977) but a
practical necessity in the modern intensive care units, which is especially the case for breakthrough seizures during induced coma where a patient may be monitored for days at a time.
It is well established how coherence (high coherence) requires synchronized (lagged or
simultaneous) bursting of neural groups, a similarity of timing (e.g., Contreras, et al., 1996),
Wavestate, Inc. – Automated Seizure Monitoring – page 14 whereas comodulation requires a similarity of amplitude, which amounts to comparable
proportions of neural groups being recruited into a rhythm under modest time restraint.
Millisecond delays have little impact on comodulation but devastate coherence (Govindan et al.,
2006). Structures which jointly inhibit proximal neural groups are more likely to produce
efficient temporal sensitivity than those which jointly inhibit distal groups. In other words, a
physical and functional bottleneck may produce the finest shared timing for the least amount of
energy. We know that the relatively compact sheathing of the reticular thalamic nucleus interacts
with thalamic neurons to synchronize large sections of thalamus and this local synchronization
propagates outward (feeds forward) and helps time-lock large swaths of cortex (Steriade et al.,
1990; Huguenard & McCormick, 2007). So the reticular thalamic nucleus may provide the
bottleneck seemingly requisite for the exquisite timing of scalp-measured coherence (minus
volume conduction).
A few seconds of multi-channel EEG contains untold information. Frequency analysis of
a single signal reduces bioelectricity to a manageable number of coefficients, but when activity
of two signals are evaluated, we are thrown back into a quagmire of comparisons and any
approach to data reduction is less certain. There are many ways that two psychophysiological
signals may be synchronized in the frequency domain and some of these properties will be
relevant to seizure detection and others may not be. The proposed periodicity table includes
concepts appropriated from physics and information sciences (Chandran, 1994; Judah & Wright,
1990). Note how property types (number of frequencies, sites, state/stability) are reduced to
concepts (e.g., magnitude, coherence) which are further reduced to specific mathematical
formulations (e.g., Goodman formula, Pearson product moment). A spectral property such as
stability can encompass many concepts (variability, correlation, kurtosis) which can be
Wavestate, Inc. – Automated Seizure Monitoring – page 15 formulated by a variety of mathematical operations, which may also require specific additional
transformation to normalize distributions for efficient application of parametric statistics.
The human brain is the most organized phenomena in nature, an entropic mix of
synchrony and freedom. This periodicity table, with its variety of spectral parameters, provides a
diligence current lacking in quantitative EEG analysis, a comprehensive system for evaluating
periodicity and synchrony previously unavailable to clinicians and scientists alike, see Figure 12.
Figure 12. Graphical depiction of temporal and spectral evaluation.
Wavestate, Inc. – Automated Seizure Monitoring – page 16 References
Arnhold J, Grassberger P, Lehnertz K, & Elger CE (1999). A robust method for detecting interdependences:
application to intracranially recorded EEG. Physica D: Nonlinear Phenomena, 134, 419-430.
Barcaro U, Denoth F, Murri L, Navona C, & Stefanini A. (1986). Changes in the interhemispheric correlation during
sleep in normal subjects. Electroencephalography and Clinical Neurophysiology, 63, 112-8.
Berger H. (1929). Ueber das Elektroenkephalogramm des Menschen. Archiv Psy Nerv, 87, 527-570.
Boeijinga PH, & Lopes da Silva FH. (1988). Differential distribution of beta and theta EEG activity in the entorhinal
cortex of the cat. Brain Research, 448, 272-86.
Bonferroni CE (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore
di Scienze Economiche e Commerciali di Firenze, 8, 3-62.
Brazier MAB & Casby J (1951). An application of the MIT digital electronic correlator to a problem in EEG.
Electroencephalography and Clinical Neurophysiology, 3, 375
Brazier, M.A.B. & Casby, J.U. (1952). Cross correlation and autocorrelation studies of electroencephalographic
potentials. EEG Clinical Neurophysiology, 4, 201-211.
Breakspear M (2002). Nonlinear phase desynchronization in human electroencephalographic data. Human Brain
Mapping, 15, 175-198.
Calvin WH (1995). Cortical columns, modules, and hebbian cell assemblies. In Arbib MA (ed), The Handbook of
Brain Theory and Neural Networks, Boston: MIT Press, pp.269-272.
Casanova MF, & Tillquist CR. (2008). Encephalization, emergent properties, and psychiatry: a minicolumnar
perspective. Neuroscientist, 14, 101-18.
Chandran V (1994). On the computation and interpretation of auto- and cross-trispectra. Acoustics, Speech, and
Signal Processing, 4, 445-448.
Contreras D, Destexhe A, Sejnowski TJ, & Steriade M (1996). Control of spatiotemporal coherence of a thalamic
oscillation by corticothalamic feedback. Science, 274, 771-4.
Cooper R, Winter AL, Crow HJ, & Walter WG. (1965). Comparison of subcortical, cortical and scalp activity using
chronically indwelling electrodes in man. Electroencephalography and Clinical Neurophysiology, 18, 217-28
David O, Cosmelli D, & Friston KJ (2004). Evaluation of different measures of functional connectivity using a
neural mass model. Neuroimage, 21, 659-73.
Dietsch G (1932). Fourier-analyse von Elektrenkephalog. des Menschen. Pflüger's Arch. ges. Physiol., 230, 106112.
Drake ME Jr, Pakalnis A, Newell SA. (1996). EEG frequency analysis in obsessive-compulsive disorder.
Neuropsychobiology, 33, 97-99.
Eberly JH & Kujawski A (1968). Relativistic statistical mechanics and blackbody radiation II. Spectral correlations.
Physics Review, 166, 197-206.
Fisher RA (1921). On the `probable error' of a coefficient of correlation deduced from a small sample. Metron, 1, 332.
Wavestate, Inc. – Automated Seizure Monitoring – page 17 Foster MR & Guinzey NJ (1967). The coefficient of coherence: Its estimation and use in geophysical data
processing. Geophysics, 32, 602-616.
Gerloff C, Bushara K, Sailer A, Wassermann EM, Chen R, Matsuoka T, Waldvogel D, Wittenberg GF, Ishii K,
Cohen LG, & Hallett M. (2006). Multimodal imaging of brain reorganization in motor areas of the contralesional
hemisphere of well recovered patients after capsular stroke. Brain, 129, 791-808.
Goodman, N.R. (1957). On the joint estimation of the spectra, cospectrum and quadrature spectrum of a twodimensional stationary Gaussian process. Dissertation, Princeton. JW Tukey advisor
Goldensohn ES, & Purpura DP (1963) Intracellular potentials of cortical neurons during focal epileptogenic
discharges. Science, 139, 840-842.
Goldstein S. (1970). Phase coherence of the alpha rhythm during photic blocking. Electroencephalography and
Clinical Neurophysiology, 29, 127-36.
Govindan RB, Raethjen J, Arning K, Kopper F, & Deuschl G (2006). Time delay and partial coherence analyses to
identify cortical connectivities. Biological cybernetics, 94, 262-75.
Guevara MA, & Corsi-Cabrera M (1996). EEG coherence or EEG correlation? International Journal of
Psychophysiology, 23, 145-53.
Hopfield JJ (1999). Brain, neural networks, and computation. Review of Modern Physics, 71, S431-S437.
Huguenard JR & McCormick DA (2007). halamic synchrony and dynamic regulation of global forebrain
oscillations. Trends in Neuroscience, 30, 350-6.
Jasper HH (1958): Report of the committee on methods of clinical examination in electroencephalography.
Electroencephalography and Clinical Neurophysiology, 10, 370-1.
Judah SR & Wright AS (1990). A dual six-port automatic network analyzer incorporating abiphase-bimodulation
element. IEEE Transactions on Microwave Theory and Techniques, 38, 238-244
Jung T-P, Makeig S, Humphries C, Lee T-W, McKeown MJ, Iragui V, &Sejnowski TJ (2000). Removing
electroencephalographic artifacts by blind source separation, Psychophysiology, 37, 163-178.
Kaiser DA (2007). A Periodicity Table: Local, network, and transformational properties of EEG Presented at 2nd
Brain Connectivity conference, Tarrytown NJ, April 22.
Kaiser DA (2008). Comodulation and coherence: empirical and theoretical differences. J Neurotherapy, 12.
Karp PJ, Katila TE, Saarinen M, Siltanen P, & Varpula TT (1980). The normal human magnetocardiogram. II. A
multipole analysis. Circulation Research, 47, 117-130
Lagerlund TD, Sharbrough FW, Busacker NE, & Cicora KM (1995). Interelectrode coherences from nearestneighbor and spherical harmonic expansion computation of laplacian of scalp potential. Electroencephalography
and Clinical Neurophysiology, 95, 178-88.
Larsen LE. (1969). An analysis of the intercorrelations among spectral amplitudes in the EEG: a generator study.
IEEE Transactions of Biomedical Engineering, 16, 23-6.
Lipping T, Ferenets R, Mortier EP, Struys MM (2007). A new method for evaluating the performance of depth-ofhypnosis indices - the D-value. Proceedings IEEE Engineering Medical & Biological Society, 1, 6487-90.
Lopes da Silva F, Pijn JP, Boeijinga P (1989). Interdependence of EEG signals: linear vs. nonlinear associations
and the significance of time delays and phase shifts. Brain Topography, 2, 9-18.
Wavestate, Inc. – Automated Seizure Monitoring – page 18 Makeig S, Jung T-P, Ghahremani D, Bell AJ, Sejnowski TJ (1996). What (Not Where) Are the Sources of the EEG?
Proceedings Cognitive Science Society, La Jolla, CA, 18, 802.
Mellors R, Vernon F, & Thomson DJ (1998). Detection of dispersive signals using multitaper double-frequency
coherence. Geophysical Journal International, 135, 146-154.
Mountcastle VB (1978). An organizing principle for cerebral function: The unit model and the distributed system. In
GM Edelman and VB Mountcastle (eds.), The Mindful Brain. Cambridge, MA: MIT Press.
Mountcastle VB. (1997). The columnar organization of the neocortex. Brain, 120, 701-22.
Mountcastle VB. (1957). Modality and topographic properties of single neurons of cat's somatic sensory cortex.
Journal of Neurophysiology, 20, 408-34.
Nunez PL, Srinivasan R, Westdorp AF, Wijesinghe RS, Tucker DM, Silberstein RB, & Cadusch PJ (1997). EEG
coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at
multiple scales. Electroencephalography & Clinical Neurophysiology, 103, 499-515.
Nunez PL, Wingeier BM, & Silberstein RB. (2001). Spatial-temporal structures of human alpha rhythms: theory,
microcurrent sources, multiscale measurements, and global binding of local networks. Human Brain Mapping, 13,
125-64.
Pearson K (1896). Mathematical contributions to the theory of evolution III. Regression, heredity and panmixia,
Philosophical Transactions of the Royal Society A, 187, 253-318.
Priestley MB (1981). Spectral Analysis and Time Series. New York: Academic Press.
Rohracher H (1937). Uber die Kurvenform cerebraler Potentialschwankungen. Pflugers Arch ges Physiol., 238, 535545.
Shaw JC (1974). An introduction to correlation and its use in signal processing. Proceedings of the
Electrophysiological Technologists Association, 21, 191-200.
Shaw JC (1981). An introduction to the coherence function and its use in EEG signal analysis, Journal of Medical
Engineering Technology, 5, 279–288.
Silberstein RB (2004). Evoked brain rhythmic activity, cortical coupling and cognition. Presented at 12th Annual
ISNR, August 26-29, Fort Lauderdale, FL.
Stam CJ &. van Dijk BW (2002). Synchronization likelihood: an unbiased measure of generalized synchronization
in multivariate data sets. Physica D, 163, 236–251.
Steriade M & Llinás RR (1988). The functional states of the thalamus and the associated neuronal interplay.
Physiological Reviews, 68, 649-742.
Steriade, M., Gloor, P., Llinas, R.R., Lopes da Silva, F.H., & Mesulam, M.-M. (1990). Basic mechanisms of
cerebral rhythmic activities. Electroencephalography and Clinical Neurophysiology, 76, 481-508.
Steriade M & Amzica F (2001). Dynamic coupling among neocortical neurons during evoked and spontaneous
spike-wave seizure activity, Thalamus & Related Systems, 1, 225-236.
Sterman MB & Kaiser DA (2001). Comodulation: A new QEEG analysis metric for assessment of structural and
functional disorders of the CNS. Journal of Neurotherapy, 4, 73-84.
Tenke CE, & Kayser J. (2005). Reference-free quantification of EEG spectra: combining current source density
(CSD) and frequency principal components analysis (fPCA). Clinical Neurophysiology, 116, 2826-46.
Wavestate, Inc. – Automated Seizure Monitoring – page 19 Tiesinga PH, & Sejnowski TJ. (2004) Rapid temporal modulation of synchrony by competition in cortical
interneuron networks. Neural Computation, 2, 251-75.
Timofeev I, Steriade M. Neocortical seizures: initiation, development and cessation. Neuroscience. 2004;123:299–
336. Journal of Neurophysiology, 72, 2051-2069.
Document by David A. Kaiser PhD, March 1, 2011.
Wavestate, Inc. – Automated Seizure Monitoring – page 20